The impact of trained patient educators on musculoskeletal clinical skills attainment in pre-clerkship medical students
Bibliographic record
Abstract
BACKGROUND: Despite the high burden of musculoskeletal (MSK) diseases, few generalists are comfortable teaching MSK physical examination (PE) skills. Patient Partners® in Arthritis (PP®IA) is a standardized patient educator program that could potentially supplement current MSK PE teaching. This study aims to determine if differences exist in MSK PE skills between non-MSK specialist physician and PP®IA taught students. METHODS: Pre-clerkship medical students attended 2-hour small group MSK PE teaching by either non-MSK specialist physician tutors or by PP®IA. All students underwent an MSK OSCE and completed retrospective pre-post questionnaires regarding comfort with MSK PE and interest in MSK. RESULTS: 83 students completed the OSCE (42 PP®IA, 41 physician taught) and 82 completed the questionnaire (42 PP®IA, 40 physician taught). There were no significant differences between groups in OSCE scores. For all questionnaire items, post-session ratings were significantly higher than pre-session ratings for both groups. In exploratory analysis PP®IA students showed significantly greater improvement in 12 of 22 questions including three of five patient-centred learning questions. CONCLUSIONS: PP®IA MSK PE teaching is as good as non-MSK specialist physician tutor teaching when measured by a five station OSCE and provide an excellent complementary resource to address current deficits in MSK PE teaching.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".